The advent of powerful servers, large-scale data storage and other information infrastructure has spurred the development of data analytics applications. Structured query language (SQL) engines, on-line analytical processing (OLAP) databases and inexpensive large disk arrays have for instance been harnessed to capture and analyze vast amounts of data.
Data analytics applications can provide many features. These can include analyzing data sets for patterns and trends. For example, sales managers can track year-over-year business performance as well as generate forecast metrics to identify behaviors that drive sales. Dashboards can be created to help visualize the results from a data analytics application. Such data analysis visualizations, however, can become problematic if, for example, the user is relatively inexperienced in operating the data analytics application or has to repeat the same or similar data analytic operations upon different data sets. This can become labor intensive and susceptible to user error when performing the same or similar data analysis operations on different data sets.